FPGA implementation of PSO based MPPT for PV systems under partial shading conditions

Kaced, Karim; Larbes, Cherif; Ait-Chikh, Salah Mohamed; Bounabi, Moussaab; Dahmane, Zine elabadine

doi:10.1109/icosc.2017.7958682

Cited by 14 publications

(5 citation statements)

References 13 publications

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“…The prognosis of the overall system is calculated based on accurate equivalent modeling. The output of the PV current is expressed mathematically as [26]:…”

Section: Pv Generatormentioning

confidence: 99%

Maximum Power Point Tracking for Brushless DC Motor-Driven Photovoltaic Pumping Systems Using a Hybrid ANFIS-FLOWER Pollination Optimization Algorithm

Priyadarshi¹,

Padmanaban

Mihet‐Popa

et al. 2018

Energies

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In this research paper, a hybrid Artificial Neural Network (ANN)-Fuzzy Logic Control (FLC) tuned Flower Pollination Algorithm (FPA) as a Maximum Power Point Tracker (MPPT) is employed to amend root mean square error (RMSE) of photovoltaic (PV) modeling. Moreover, Gaussian membership functions have been considered for fuzzy controller design. This paper interprets the Luo converter occupied brushless DC motor (BLDC)-directed PV water pump application. Experimental responses certify the effectiveness of the suggested motor-pump system supporting diverse operating states. The Luo converter, a newly developed DC-DC converter, has high power density, better voltage gain transfer and superior output waveform and can track optimal power from PV modules. For BLDC speed control there is no extra circuitry, and phase current sensors are enforced for this scheme. The most recent attempt using adaptive neuro-fuzzy inference system (ANFIS)-FPA-operated BLDC directed PV pump with advanced Luo converter, has not been formerly conferred.

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“…The prognosis of the overall system is calculated based on accurate equivalent modeling. The output of the PV current is expressed mathematically as [26]:…”

Section: Pv Generatormentioning

confidence: 99%

Maximum Power Point Tracking for Brushless DC Motor-Driven Photovoltaic Pumping Systems Using a Hybrid ANFIS-FLOWER Pollination Optimization Algorithm

Priyadarshi¹,

Padmanaban

Mihet‐Popa

et al. 2018

Energies

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“…Swarm-intelligence-based optimization techniques, which are a subset of BI optimization technology [7], offer a solution. These methods find the optimal global solution by constantly comparing a lot of solutions, including particle swarm optimization (PSO) [8], Ant Colony Optimization (ACO) [9], Cuckoo Search Algorithms (CSAs) [10], Artificial Bee Colony (ABC) [11], Gray Wolf Optimization (GWO) [12], and Salp Swarm Optimization (SSO) [13], among others. Compared to other technologies, MPPT technology based on swarm-intelligence optimization exhibits faster response speeds and higher efficiency in achieving the GMPPT under PSCs.…”

Section: Introductionmentioning

confidence: 99%

“…The new position of the ith cuckoo is obtained by updating the levy-flight strategy according to Equation (8).…”

Section: Introduction Of Cuckoo Search Algorithmmentioning

confidence: 99%

Novel Global MPPT Technique Based on Hybrid Cuckoo Search and Artificial Bee Colony under Partial-Shading Conditions

Qi,

Xia,

Cai

et al. 2024

Electronics

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Under partial-shading conditions (PSCs), the output P-V curve of the photovoltaic array shows a multi-peak shape. This poses a challenge for traditional maximum power point tracking (MPPT) algorithms to accurately track the global maximum power point (GMPP). Single intelligent algorithms such as PSO and ABC have difficulty balancing tracking speed and tracking accuracy. Additionally, there is significant power oscillation during the tracking process. Therefore, this paper proposes a new hybrid method called the Cuckoo Search Algorithm and Artificial Bee Colony algorithm (CSA-ABC) for photovoltaic MPPT. The CSA-ABC algorithm combines the local random walk and the global levy flight mechanism of the cuckoo algorithm, by probability selection, to decide whether to group the population, and introduces adaptive weight factors and gravitational mechanisms between adjacent individuals, incorporating an algorithm restart mechanism to track new MPPs in response to changes in the external environment. The algorithm is implemented in MATLAB/Simulink using a photovoltaic power-generation system model. Simulation verification is performed under different PSC scenarios. The results show that the proposed MPPT algorithm is 6.2–78.6% faster than the PSO, CSA, and ABC algorithms and two other hybrid algorithms, with a smaller power oscillation during the tracking process and zero power oscillation during the steady process.

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“…Metaheuristic algorithms offer a compelling solution in this regard. These techniques, such as Particle Swarm Optimization (PSO) [27,28], Genetic Algorithms (GAs) [29,30], Simulated Annealing (SA) [31], and Ant Colony Optimization (ACO) [32], Grey wolf optimization (GWO) [33], Crew Search Algorithm (CSA) [15], and Cuckoo Search (CS) algorithm [16], and others, have shown to be highly effective in GMPPT under PSCs. These algorithms offer flexibility and are capable of exploring a broad solution space to find near-optimal solutions in a reasonable time frame.…”

Section: Introductionmentioning

confidence: 99%

A New Efficient Cuckoo Search MPPT Algorithm Based on a Super-Twisting Sliding Mode Controller for Partially Shaded Standalone Photovoltaic System

et al. 2023

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The impact of Partial Shading Conditions (PSCs) significantly influences the output of Photovoltaic Systems (PVSs). Under PSCs, the Power-Voltage (P-V) characteristic of the PVS unveils numerous power peaks, inclusive of local maxima and a global maximum. The latter represents the optimum power point. Traditional Maximum Power Point Tracking (MPPT) algorithms struggle to track the Global Maximum Power Point (GMPP). To address this, our study emphasizes the creation of a novel algorithm capable of identifying the GMPP. This approach combines the Cuckoo Search (CS) MPPT algorithm with an Integral Super-Twisting Sliding Mode Controller (STSMC) using their benefits to enhance the PVS performance under PSCs in terms of high efficiency, low power losses, and high-speed convergence towards the GMPP. The STSMC is a second-order Sliding Mode Control strategy that employs a continuous control action that attenuates the “chattering” phenomenon, caused when the first-order SMC technique is employed. Indeed, the proposed CS-STSMC-MPPT algorithm consists of two parts. The first one is based on the CS algorithm used for scanning the power-voltage curve to identify the GMPP, and subsequently generating the associated optimal voltage reference. The second part aims to track the voltage reference by manipulating the duty cycle of the boost converter. The proposed CS-STSMC-MPPT algorithm is featured by its strength against uncertainties and modeling errors. The obtained simulation results underline a high convergence speed and an excellent precision of the proposed method in identifying and tracking the GMPP with high efficiency under varying shading scenarios. For comparative purposes, this method is set against the hybrid CS-Proportional Integral Derivative, the conventional CS, the Particle Swarm Optimization, and the Perturb and Observe algorithms under different PSCs, including zero, weak, and severe shading. Simulation conducted in the Matlab/Simulink environment confirms the superior performance of the proposed CS-STSMC-MPPT algorithm in terms of precision, convergence speed, efficiency, and resilience.

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FPGA implementation of PSO based MPPT for PV systems under partial shading conditions

Cited by 14 publications

References 13 publications

Maximum Power Point Tracking for Brushless DC Motor-Driven Photovoltaic Pumping Systems Using a Hybrid ANFIS-FLOWER Pollination Optimization Algorithm

Maximum Power Point Tracking for Brushless DC Motor-Driven Photovoltaic Pumping Systems Using a Hybrid ANFIS-FLOWER Pollination Optimization Algorithm

Novel Global MPPT Technique Based on Hybrid Cuckoo Search and Artificial Bee Colony under Partial-Shading Conditions

A New Efficient Cuckoo Search MPPT Algorithm Based on a Super-Twisting Sliding Mode Controller for Partially Shaded Standalone Photovoltaic System

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